Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Fast Fourier Transform01:10

Fast Fourier Transform

1.3K
The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
1.3K
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

501
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
501
Upsampling01:22

Upsampling

743
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
743
Aliasing01:18

Aliasing

929
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
929
Convergence of Fourier Series01:21

Convergence of Fourier Series

608
The Fourier series is a powerful mathematical tool for representing periodic signals as an infinite sum of complex exponentials. In practice, this infinite series is truncated to a finite number of terms, yielding a partial sum. This truncation makes the approximation of the signal feasible but introduces certain challenges, particularly near discontinuities, known as the Gibbs phenomenon.
The Gibbs phenomenon refers to the persistent oscillations and overshoots that occur near discontinuities...
608
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

910
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
910

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A Robust Complex <i>α</i>-Sigmoid Affine Projection Algorithm Under Non-Gaussian Noise.

Sensors (Basel, Switzerland)·2026
Same author

A family of constrained maximum mixture total correntropy algorithms for adaptive filtering.

ISA transactions·2025
Same author

Adaptive learning algorithm and its convergence analysis with complex-valued error loss network.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Complex quantized minimum error entropy with fiducial points: theory and application in model regression.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Generalized Maximum Complex Correntropy Augmented Adaptive IIR Filtering.

Entropy (Basel, Switzerland)·2022
Same author

Comprehensive genetic, clinical and electrophysiological studies of familial cortical myoclonic tremor with epilepsy 1 highlight the role of gene configurations.

Seizure·2021
Same journal

Dynamic analysis and reliable mechanical optimization application of ring HNN effected with a memristive neuron.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

DAFF-Net: A detection and search method for small-scale low surface brightness galaxies.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Quasi-synchronization for complex networks with hybrid pinning intermittent control.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Physics-encoded convolutional neural operators for parametric PDEs: A convergence-guaranteed framework via pre-computed kernel fields.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Exploiting audio-visual modalities in videos: Object detection via multi-stage bilateral coupling network.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Reliability-aware modality completion with cross-modal distillation for federated learning with missing modalities.

Neural networks : the official journal of the International Neural Network Society·2026
See all related articles

Related Experiment Video

Updated: Apr 23, 2026

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
07:11

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

Published on: August 19, 2021

3.3K

On extending the complex FastICA algorithms to noisy data.

Zongli Ruan1, Liping Li2, Guobing Qian2

  • 1School of Electronic Engineering, University of Electronics Science and Technology of China, Chengdu 611731, China; College of Science, China University of Petroleum, Qingdao 266580, China.

Neural Networks : the Official Journal of the International Neural Network Society
|September 23, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces modified complex FastICA algorithms to effectively handle noise in digital signal processing. These enhanced methods improve the separation of independent components from noisy data using kurtosis and negentropy.

Keywords:
Complex fast fixed-point algorithmIndependent component analysis (ICA)Noisy dataPseudo-whiteningStability condition

Related Experiment Videos

Last Updated: Apr 23, 2026

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
07:11

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

Published on: August 19, 2021

3.3K

Area of Science:

  • Digital Signal Processing
  • Statistical Signal Processing
  • Machine Learning

Background:

  • Independent Component Analysis (ICA) is crucial for signal separation.
  • Complex-valued FastICA algorithms are significant but often neglect noise.
  • Existing models require enhancement for real-world noisy data.

Purpose of the Study:

  • To develop and evaluate complex ICA algorithms robust to noise.
  • To modify existing FastICA algorithms for noisy data scenarios.
  • To analyze the stability of cost functions in noisy conditions.

Main Methods:

  • Modification of nc-FastICA and KM-F algorithms.
  • Utilizing kurtosis and negentropy-based cost functions.
  • Analysis of algorithm stability conditions.

Main Results:

  • Demonstrated effectiveness of modified algorithms (nc-FastICA, KM-F) in separating components from noisy data.
  • Provided stability conditions for the cost functions used.
  • Simulations confirmed the improved performance in noisy environments.

Conclusions:

  • The proposed complex FastICA algorithms effectively address noise in signal separation.
  • The modified algorithms offer improved performance and stability for noisy datasets.
  • This work advances ICA applications in noisy digital signal processing.